L band microwave remote sensing and land data assimilation improve the representation of prestorm soil moisture conditions for hydrologic forecasting. Issue 11 (3rd June 2017)

• Record Type: Journal Article
• Authors: Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.
• Is Part Of: Geophysical research letters. Volume 44:Issue 11(2017)
• Page Start: 5495
• Page End: 5503
• Publication Date: 2017-06-03
• Subjects: hydrologic forecasting -- soil moisture -- remote sensing -- data assimilation; Geophysics -- Periodicals; Planets -- Periodicals; Lunar geology -- Periodicals; 550
• DOI: 10.1002/2017GL073642
• Languages: English
• ISSNs: 0094-8276

Abstract

Abstract: Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm‐scale runoff ratio (i.e., total streamflow divided by total rainfall accumulation in depth units) and prestorm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite‐based, L band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting streamflow response to future rainfall events. Plain Language Summary: Forecasting streamflow conditions is important for minimizing loss of life and property during flooding and adequately planning for low streamflow conditions accompanying drought. One way to improve these forecasts is measuring the amount of water in the soil—since soil moisture conditions determine what fraction of rainfall will run off horizontally into stream channels (versus vertically infiltrate into the soil column). Within the past 5 years, there have been important advances in our ability to monitor soil moisture over large scales using both satellite‐based sensors and the application of new land data assimilation techniques. This paper illustrates that these advances have significantly improved our capacity to forecast how much streamflow will be generated by future precipitation events. These results may eventually be used by operational forecasters to improve flash flood forecasting and agricultural water use management. Key Points: Knowledge of current soil moisture conditions is important for forecasting future streamflow The past 5 years has seen the introduction of new soil moisture data sets based on L band remote sensing and data assimilation These data sets represent a significant advance in our ability to forecast streamflow using available soil moisture data sets